{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e6c17397",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>team</th>\n",
       "      <th>score</th>\n",
       "      <th>sanfen</th>\n",
       "      <th>faqiu</th>\n",
       "      <th>changci</th>\n",
       "      <th>shijian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>乔尔-恩比德</td>\n",
       "      <td>76人</td>\n",
       "      <td>30.6</td>\n",
       "      <td>1.40-3.70</td>\n",
       "      <td>9.60-11.80</td>\n",
       "      <td>68</td>\n",
       "      <td>33.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>扬尼斯-阿德托昆博</td>\n",
       "      <td>雄鹿</td>\n",
       "      <td>29.9</td>\n",
       "      <td>1.10-3.60</td>\n",
       "      <td>8.20-11.40</td>\n",
       "      <td>67</td>\n",
       "      <td>32.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>特雷-杨</td>\n",
       "      <td>老鹰</td>\n",
       "      <td>28.4</td>\n",
       "      <td>3.10-8.00</td>\n",
       "      <td>6.60-7.30</td>\n",
       "      <td>76</td>\n",
       "      <td>34.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>卢卡-东契奇</td>\n",
       "      <td>独行侠</td>\n",
       "      <td>28.4</td>\n",
       "      <td>3.10-8.80</td>\n",
       "      <td>5.60-7.50</td>\n",
       "      <td>65</td>\n",
       "      <td>35.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>德马尔-德罗赞</td>\n",
       "      <td>公牛</td>\n",
       "      <td>27.9</td>\n",
       "      <td>0.70-1.90</td>\n",
       "      <td>6.80-7.80</td>\n",
       "      <td>76</td>\n",
       "      <td>36.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>孙晨然</td>\n",
       "      <td>北京</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0-0.0</td>\n",
       "      <td>0.0-0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>于德豪</td>\n",
       "      <td>深圳</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0-0.0</td>\n",
       "      <td>0.0-0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>刘旭乘</td>\n",
       "      <td>广东</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0-1.0</td>\n",
       "      <td>0.0-0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>李佳益</td>\n",
       "      <td>北京</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0-0.0</td>\n",
       "      <td>0.0-0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>杨凯</td>\n",
       "      <td>青岛</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0-0.0</td>\n",
       "      <td>0.0-0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>866 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          name team  score     sanfen       faqiu  changci  shijian\n",
       "0       乔尔-恩比德  76人   30.6  1.40-3.70  9.60-11.80       68     33.8\n",
       "1    扬尼斯-阿德托昆博   雄鹿   29.9  1.10-3.60  8.20-11.40       67     32.9\n",
       "2         特雷-杨   老鹰   28.4  3.10-8.00   6.60-7.30       76     34.9\n",
       "3       卢卡-东契奇  独行侠   28.4  3.10-8.80   5.60-7.50       65     35.4\n",
       "4      德马尔-德罗赞   公牛   27.9  0.70-1.90   6.80-7.80       76     36.1\n",
       "..         ...  ...    ...        ...         ...      ...      ...\n",
       "861        孙晨然   北京    0.0    0.0-0.0     0.0-0.0        0      0.0\n",
       "862        于德豪   深圳    0.0    0.0-0.0     0.0-0.0        0      0.0\n",
       "863        刘旭乘   广东    0.0    0.0-1.0     0.0-0.0        1      3.0\n",
       "864        李佳益   北京    0.0    0.0-0.0     0.0-0.0        0      0.0\n",
       "865         杨凯   青岛    0.0    0.0-0.0     0.0-0.0        1     -3.0\n",
       "\n",
       "[866 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# 读取数据\n",
    "data = pd.read_csv(\"lanqiumingxing.csv\",encoding=\"gbk\",names=['name','team','score','sanfen','faqiu','changci','shijian'])\n",
    "data\n",
    "#增加列头：ranking name team score sanfen  faqiu  changci shijian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a6ea68ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    866.000000\n",
       "mean       8.367898\n",
       "std        7.763119\n",
       "min        0.000000\n",
       "25%        2.700000\n",
       "50%        5.800000\n",
       "75%       12.600000\n",
       "max       44.100000\n",
       "Name: score, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看“得分”列的基本统计\n",
    "# 总数据数 平均数 标准差 最小  25% 50% 75%各有多少条  最大\n",
    "data.score.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dc9bb94c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60.26602031798566"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据方差\n",
    "data.score.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "25b252ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.367898383371823"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算“得分”的平均成绩\n",
    "import numpy as np\n",
    "np.average(data['score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "860a5327",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\28497\\AppData\\Local\\Temp/ipykernel_65488/3909270154.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  data.median()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "score       5.80\n",
       "changci    26.50\n",
       "shijian     6.52\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算中位数\n",
    "data.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "77fec77a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "team\n",
       "76人     21.766667\n",
       "上海       7.941463\n",
       "八一       7.605556\n",
       "公牛      23.300000\n",
       "凯尔特人    25.250000\n",
       "勇士      20.400000\n",
       "北京       6.250000\n",
       "北控       6.590698\n",
       "吉林       7.736842\n",
       "同曦       7.863415\n",
       "四川       7.402381\n",
       "国王      19.800000\n",
       "天津       6.634884\n",
       "太阳      22.000000\n",
       "奇才      17.100000\n",
       "宁波       5.191667\n",
       "尼克斯     20.050000\n",
       "山东       8.421053\n",
       "山西       7.146512\n",
       "广东       8.460526\n",
       "广厦       8.223256\n",
       "广州       7.912821\n",
       "快船      16.800000\n",
       "掘金      27.100000\n",
       "新疆       9.072973\n",
       "森林狼     21.333333\n",
       "江苏       7.631707\n",
       "活塞      16.750000\n",
       "浙江       7.634211\n",
       "深圳       8.290244\n",
       "湖人      18.500000\n",
       "火箭      17.600000\n",
       "灰熊      17.250000\n",
       "热火      20.700000\n",
       "爵士      22.000000\n",
       "独行侠     22.350000\n",
       "猛龙      20.466667\n",
       "福建       8.106818\n",
       "老鹰      28.400000\n",
       "辽宁       7.800000\n",
       "雄鹿      22.766667\n",
       "青岛       7.364103\n",
       "马刺      19.050000\n",
       "骑士      21.700000\n",
       "魔术      16.300000\n",
       "鹈鹕      17.800000\n",
       "黄蜂      19.866667\n",
       "Name: score, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#分组分析\n",
    "#import numpy as np\n",
    "#from pandas import read_csv\n",
    "#df = pd.read_csv(\"NBAqiuyuandefen.csv\",encoding=\"gbk\",names=['name','team','score','sanfen','faqiu',)\n",
    "#df\n",
    "# 根据球队分组求得分的平均值\n",
    "data.groupby( 'team')['score'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "06fb6fd9",
   "metadata": {},
   "outputs": [
    {
     "ename": "SpecificationError",
     "evalue": "nested renamer is not supported",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mSpecificationError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_65488/2046111591.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m data.groupby(by=['team'])['score'].agg({'总分':np.sum,\n\u001b[0m\u001b[0;32m      2\u001b[0m                                      \u001b[1;34m'人数'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m                                      \u001b[1;34m'平均值'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m                                      \u001b[1;34m'方差'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m                                      \u001b[1;34m'标准差'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\generic.py\u001b[0m in \u001b[0;36maggregate\u001b[1;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    247\u001b[0m             \u001b[1;31m# but not the class list / tuple itself.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    248\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmaybe_mangle_lambdas\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 249\u001b[1;33m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_aggregate_multiple_funcs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    250\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mrelabeling\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    251\u001b[0m                 \u001b[1;31m# error: Incompatible types in assignment (expression has type\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\generic.py\u001b[0m in \u001b[0;36m_aggregate_multiple_funcs\u001b[1;34m(self, arg)\u001b[0m\n\u001b[0;32m    282\u001b[0m             \u001b[1;31m# have not shown a higher level one\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    283\u001b[0m             \u001b[1;31m# GH 15931\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 284\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mSpecificationError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"nested renamer is not supported\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    285\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    286\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mSpecificationError\u001b[0m: nested renamer is not supported"
     ]
    }
   ],
   "source": [
    "data.groupby(by=['team'])['score'].agg({'总分':np.sum,\n",
    "                                     '人数':np.size,\n",
    "                                     '平均值':np.mean,\n",
    "                                     '方差':np.var,\n",
    "                                     '标准差':np.std,\n",
    "                                     '最大值':np.max,\n",
    "                                     '最小值':np.min})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2fe9a77e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.40-3.709.60-11.80\n",
       "1    1.10-3.608.20-11.40\n",
       "2     3.10-8.006.60-7.30\n",
       "3     3.10-8.805.60-7.50\n",
       "4     0.70-1.906.80-7.80\n",
       "Name: 总分, dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#分布分析\n",
    "#df.head()\n",
    "data['总分']=data.sanfen+data.faqiu\n",
    "data['总分'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "bdcb56c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                866\n",
       "unique               728\n",
       "top       0.0-0.00.0-0.0\n",
       "freq                 107\n",
       "Name: 总分, dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['总分'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "56509106",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-1.0, 20, 30, 45.1]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将“得分”数据分为三段\n",
    "bins = [min(data.score)-1,20,30,max(data.score)+1]\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "dbea61c1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['20及其以下', '20到30', '30及其以上']"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#给三段数据贴标签\n",
    "labels=['20及其以下','20到30','30及其以上']\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f045c671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    30及其以上\n",
       "1     20到30\n",
       "2     20到30\n",
       "3     20到30\n",
       "4     20到30\n",
       "Name: score, dtype: category\n",
       "Categories (3, object): ['20及其以下' < '20到30' < '30及其以上']"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "得分分层 = pd.cut(data.score,bins,labels=labels)\n",
    "得分分层.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b2a384c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.tail of           name team  score     sanfen       faqiu  changci  shijian  \\\n",
       "0       乔尔-恩比德  76人   30.6  1.40-3.70  9.60-11.80       68     33.8   \n",
       "1    扬尼斯-阿德托昆博   雄鹿   29.9  1.10-3.60  8.20-11.40       67     32.9   \n",
       "2         特雷-杨   老鹰   28.4  3.10-8.00   6.60-7.30       76     34.9   \n",
       "3       卢卡-东契奇  独行侠   28.4  3.10-8.80   5.60-7.50       65     35.4   \n",
       "4      德马尔-德罗赞   公牛   27.9  0.70-1.90   6.80-7.80       76     36.1   \n",
       "..         ...  ...    ...        ...         ...      ...      ...   \n",
       "861        孙晨然   北京    0.0    0.0-0.0     0.0-0.0        0      0.0   \n",
       "862        于德豪   深圳    0.0    0.0-0.0     0.0-0.0        0      0.0   \n",
       "863        刘旭乘   广东    0.0    0.0-1.0     0.0-0.0        1      3.0   \n",
       "864        李佳益   北京    0.0    0.0-0.0     0.0-0.0        0      0.0   \n",
       "865         杨凯   青岛    0.0    0.0-0.0     0.0-0.0        1     -3.0   \n",
       "\n",
       "                      总分    得分分层  \n",
       "0    1.40-3.709.60-11.80  30及其以上  \n",
       "1    1.10-3.608.20-11.40   20到30  \n",
       "2     3.10-8.006.60-7.30   20到30  \n",
       "3     3.10-8.805.60-7.50   20到30  \n",
       "4     0.70-1.906.80-7.80   20到30  \n",
       "..                   ...     ...  \n",
       "861       0.0-0.00.0-0.0  20及其以下  \n",
       "862       0.0-0.00.0-0.0  20及其以下  \n",
       "863       0.0-1.00.0-0.0  20及其以下  \n",
       "864       0.0-0.00.0-0.0  20及其以下  \n",
       "865       0.0-0.00.0-0.0  20及其以下  \n",
       "\n",
       "[866 rows x 9 columns]>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['得分分层'] = 得分分层\n",
    "data.tail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "de409b51",
   "metadata": {},
   "outputs": [
    {
     "ename": "SpecificationError",
     "evalue": "nested renamer is not supported",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mSpecificationError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_65488/3694240523.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mby\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'得分分层'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'score'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'人数'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\generic.py\u001b[0m in \u001b[0;36maggregate\u001b[1;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[0;32m    247\u001b[0m             \u001b[1;31m# but not the class list / tuple itself.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    248\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmaybe_mangle_lambdas\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 249\u001b[1;33m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_aggregate_multiple_funcs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    250\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mrelabeling\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    251\u001b[0m                 \u001b[1;31m# error: Incompatible types in assignment (expression has type\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\generic.py\u001b[0m in \u001b[0;36m_aggregate_multiple_funcs\u001b[1;34m(self, arg)\u001b[0m\n\u001b[0;32m    282\u001b[0m             \u001b[1;31m# have not shown a higher level one\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    283\u001b[0m             \u001b[1;31m# GH 15931\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 284\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mSpecificationError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"nested renamer is not supported\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    285\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    286\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mSpecificationError\u001b[0m: nested renamer is not supported"
     ]
    }
   ],
   "source": [
    "data.groupby(by=['得分分层'])['score'].agg({'人数':np.size})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e8310808",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>changci</th>\n",
       "      <th>score</th>\n",
       "      <th>shijian</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>team</th>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">76人</th>\n",
       "      <th>乔尔-恩比德</th>\n",
       "      <td>68.0</td>\n",
       "      <td>30.6</td>\n",
       "      <td>33.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>托拜厄斯-哈里斯</th>\n",
       "      <td>73.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>34.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>泰雷斯-马克西</th>\n",
       "      <td>75.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>35.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">上海</th>\n",
       "      <th>任骏威</th>\n",
       "      <td>42.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>何重达</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>魔术</th>\n",
       "      <th>科尔-安东尼</th>\n",
       "      <td>65.0</td>\n",
       "      <td>16.3</td>\n",
       "      <td>31.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>鹈鹕</th>\n",
       "      <th>约纳斯-瓦兰丘纳斯</th>\n",
       "      <td>74.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>30.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">黄蜂</th>\n",
       "      <th>拉梅洛-鲍尔</th>\n",
       "      <td>75.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>32.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>特里-罗齐尔</th>\n",
       "      <td>73.0</td>\n",
       "      <td>19.3</td>\n",
       "      <td>33.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>迈尔斯-布里奇斯</th>\n",
       "      <td>80.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>35.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>696 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                changci  score  shijian\n",
       "team name                              \n",
       "76人  乔尔-恩比德        68.0   30.6     33.8\n",
       "     托拜厄斯-哈里斯      73.0   17.2     34.8\n",
       "     泰雷斯-马克西       75.0   17.5     35.3\n",
       "上海   任骏威           42.0   11.0     13.1\n",
       "     何重达            2.0    2.0      1.0\n",
       "...                 ...    ...      ...\n",
       "魔术   科尔-安东尼        65.0   16.3     31.7\n",
       "鹈鹕   约纳斯-瓦兰丘纳斯     74.0   17.8     30.3\n",
       "黄蜂   拉梅洛-鲍尔        75.0   20.1     32.3\n",
       "     特里-罗齐尔        73.0   19.3     33.7\n",
       "     迈尔斯-布里奇斯      80.0   20.2     35.5\n",
       "\n",
       "[696 rows x 3 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#交叉分析（透视表）\n",
    "from pandas import pivot_table\n",
    "data.pivot_table(index=['team','name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "54d4590a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>changci</th>\n",
       "      <th>shijian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.526678</td>\n",
       "      <td>0.940438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>changci</th>\n",
       "      <td>0.526678</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.624566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shijian</th>\n",
       "      <td>0.940438</td>\n",
       "      <td>0.624566</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            score   changci   shijian\n",
       "score    1.000000  0.526678  0.940438\n",
       "changci  0.526678  1.000000  0.624566\n",
       "shijian  0.940438  0.624566  1.000000"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算相关系数矩阵，即计算出任意两数据之间的相关系数\n",
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2d2ef6f2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9404375616748881"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#时间和得分的相关系数比较大\n",
    "data['score'].corr(data['shijian'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "27223902",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt.rcParams['font.family'] = ['sans-serif']\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.plot(data['name'],data['score'])\n",
    "# 添加x 轴--name   y轴--deifen 标签\n",
    "plt.ylabel(\"y-得分\")\n",
    "#添加标题\n",
    "plt.title(\"篮球明星球员得分数据折线分析图\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "65607709",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      name  score  sanfen  faqiu  changci  shijian  总分  得分分层\n",
      "team                                                        \n",
      "76人      3      3       3      3        3        3   3     3\n",
      "上海      41     41      41     41       41       41  41    41\n",
      "八一      18     18      18     18       18       18  18    18\n",
      "公牛       3      3       3      3        3        3   3     3\n",
      "凯尔特人     2      2       2      2        2        2   2     2\n",
      "勇士       3      3       3      3        3        3   3     3\n",
      "北京      46     46      46     46       46       46  46    46\n",
      "北控      43     43      43     43       43       43  43    43\n",
      "吉林      38     38      38     38       38       38  38    38\n",
      "同曦      41     41      41     41       41       41  41    41\n",
      "四川      42     42      42     42       42       42  42    42\n",
      "国王       2      2       2      2        2        2   2     2\n",
      "天津      43     43      43     43       43       43  43    43\n",
      "太阳       2      2       2      2        2        2   2     2\n",
      "奇才       1      1       1      1        1        1   1     1\n",
      "宁波      24     24      24     24       24       24  24    24\n",
      "尼克斯      2      2       2      2        2        2   2     2\n",
      "山东      38     38      38     38       38       38  38    38\n",
      "山西      43     43      43     43       43       43  43    43\n",
      "广东      38     38      38     38       38       38  38    38\n",
      "广厦      43     43      43     43       43       43  43    43\n",
      "广州      39     39      39     39       39       39  39    39\n",
      "快船       1      1       1      1        1        1   1     1\n",
      "掘金       1      1       1      1        1        1   1     1\n",
      "新疆      37     37      37     37       37       37  37    37\n",
      "森林狼      3      3       3      3        3        3   3     3\n",
      "江苏      41     41      41     41       41       41  41    41\n",
      "活塞       2      2       2      2        2        2   2     2\n",
      "浙江      38     38      38     38       38       38  38    38\n",
      "深圳      41     41      41     41       41       41  41    41\n",
      "湖人       1      1       1      1        1        1   1     1\n",
      "火箭       2      2       2      2        2        2   2     2\n",
      "灰熊       2      2       2      2        2        2   2     2\n",
      "热火       1      1       1      1        1        1   1     1\n",
      "爵士       2      2       2      2        2        2   2     2\n",
      "独行侠      2      2       2      2        2        2   2     2\n",
      "猛龙       3      3       3      3        3        3   3     3\n",
      "福建      44     44      44     44       44       44  44    44\n",
      "老鹰       1      1       1      1        1        1   1     1\n",
      "辽宁      39     39      39     39       39       39  39    39\n",
      "雄鹿       3      3       3      3        3        3   3     3\n",
      "青岛      39     39      39     39       39       39  39    39\n",
      "马刺       2      2       2      2        2        2   2     2\n",
      "骑士       1      1       1      1        1        1   1     1\n",
      "魔术       1      1       1      1        1        1   1     1\n",
      "鹈鹕       1      1       1      1        1        1   1     1\n",
      "黄蜂       3      3       3      3        3        3   3     3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndata=data.groupby(\"team\").count()\n",
    "print(ndata)\n",
    "#获取索引\n",
    "# ndata.index\n",
    "# 中文处理\n",
    "plt.rcParams['font.family']=['sans-serif']\n",
    "plt.bar(ndata.index,ndata['name'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f1243e0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '篮球明星球员三分球命中个数数据散点分析图')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.family']=['sans-serif']\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.scatter(data['name'],data['sanfen'])\n",
    "# 添加x 轴--name   y轴--sanfen 标签\n",
    "plt.xlabel(\"x-球员\")\n",
    "plt.ylabel(\"y-三分命中个数\")\n",
    "#添加标题\n",
    "plt.title(\"篮球明星球员三分球命中个数数据散点分析图\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d5314b1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "plt.figure(figsize=(3,3))\n",
    "sns.heatmap(data.corr(),annot=True,fmt=\".3f\",cmap=\"coolwarm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f31ecc41",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70e21587",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7f42ab2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d3be629",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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